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Hi everyone,

I’m building Persona, a platform to delegate email scheduling to AI. Lately, I’ve been working hard to get those first users on board, but it’s been quite challenging.

I’ve already tried the typical strategies that everybody talks about: cold email, LinkedIn InMail, careful targeting, decent copy. It’s mostly been a dead end. Low open rates, almost no replies.

At this point, I’m not looking for the usual advice you see in blog posts or on reddit. I’m specifically curious about unconventional or non-obvious things that actually worked for you early on, especially things that felt a bit scrappy, weird, or counterintuitive at the time.

If you’ve been through this phase, what genuinely worked and got you your first users?

"Debugging is twice as hard as writing the code in the first place. Therefore, if you write the code as cleverly as possible, you are, by definition, not smart enough to debug it"

This has been a timely PSA.

Currently the GitHub status page says there is a "Major Outage" for GitHub Actions.

https://www.githubstatus.com/

This is as of 19:58 UTC / 11:58 PST on 2-Feb-2026

To make AI agents more efficient, we need to build feedback loops with real systems: deployments, logs, configs, environments, dashboards.

But this is where things break down.

Most modern apps don’t have fine-grained permissions.

Concrete example: Vercel. If I want an agent to read logs or inspect env vars, I have to give it a token that also allows it to modify or delete things. There’s no clean read-only or capability-scoped access.

And this isn’t just Vercel. I see the same pattern across cloud dashboards, CI/CD systems, and SaaS APIs that were designed around trusted humans, not autonomous agents.

So the real question:

How are people actually restricting AI agents in production today?

Are you building proxy layers that enforce policy? Wrapping APIs with allowlists? Or just accepting the risk?

It feels like we’re trying to connect autonomous systems to infrastructure that was never designed for them.

Curious how others are handling this in real setups, not theory.

Started with actions runners not picking up new jobs and now it spread to other services. - https://www.githubstatus.com/

Could not post it earlier as it was flagged as a duplicate of an incident from last month.

Facebook's feed is nonfunctional. Only some people get notification, even though they have notification for all messages turned on. Only some get the newest posts in their main feed. Sometimes I do get notifications, but only long after an urgent message was posted.

Has anybody successfully moved their local community off of Facebook groups?

I'm thinking about neighbor conversations/events, daycare, kindergarten, kids' classmates, sporting communities, etc.

If so, where did you go? Did you build something yourself or do you self-host some open source project? Did you find a good paid alternative?

My Google account has been suspended from GCP since March 2024.

I have submitted multiple appeals through ts-consult@google.com over 2 years. Every time I get the same automated template asking me to explain, I reply with details, then nothing. No human ever responds.

Case: #1-8622000037271

Timeline: - March 2024: Suspended, appeal submitted - April 2024: Automated requests for info, I replied - Nov 2024: More automated emails, I replied again - Dec 2024 - now: Complete silence

I am a CS researcher at UC Berkeley. This has seriously impacted my work.

Has anyone successfully gotten Google to review a GCP suspension appeal? How do you reach a human?

I am curious I am not sure if AI is just hype, I use it for software and a few other things. But looking at so many people talking about AGI when the best models can't even answer simple stuff correctly, fail at tool use, are vulnerable to all types of injection attacks that don't make sense.

I don't know if the investments in AI are worth it but am I blind for not seeing any hope for AGI any time soon.

Agentic AI is interesting perhaps but I hardly have had it work perfectly, I have to hold it's hand at everything.

People making random claims about AGI soon is really weakening my confidence in AI in general. Given I haven't seen much improvements in last few years other than better tools and wrappers, and models that work better with these tools and wrappers.

I’ve noticed that most physical scientific and graphing calculators are easily outdone in terms of performance, capability and ease of use by the likes of Desmos and the default calculators on OS’es like the iOS, Android, and Windows.

It kind of makes me wonder whether people still use physical calculators from Texas Instruments, Casio, etc

If you do, I’d love to know why and how it is different/better for you than the ones I’ve mentioned and others like them and vice verse.

Cheers!

I'm genuinely curious. I've read a lot of stories of people adding it to group chats, using it as a remote coding and research assistant, or getting it to clean up folders and inboxes. This is all interesting, but nothing that couldn't have been accomplished with the tooling that was already in place.

I'd like to hear stories like "it accurately filed my taxes", "it organized and booked all tickets for my trip from A to B", or "it successfully executed a cold email campaign with a 30% open rate". These stories must be out there - would you mind sharing them?

Hello those who still read forums.

I have recently graduated from a college and started working as a junior dev (trying to consume as much knowledge from senior colleagues as I can now) and it seems that the real world is kind of a different story compared to the college practice.

In the college we've been taught about design patterns and all these responsibilities like domain, application, infrastructure, UI. Domain should never depend on infrastructure or application layer and so on. But the projects I got have domain that depend on infrastructure and another one where application has a reference directly to infrastructure and been told that this is correct implementation... doh..

I think I was kind of a good at listening for the lectures, but I now am doubting about, whether it was worth learning stuff at all lol since it's so controversial out there. I am, of course, in no position to question senior dev, but what do you guys think - is it really normal that all the college so called "best practices" go straight to the trash bin or am I just misunderstanding the real-work-like context?

Salutations HN!

I’ll keep it simple, has anyone known of or been a part of a co-op of software engineers? And if so, how did it go?

I’m curious because it seems like the vast majority of early capital raises seem to go to paying for software development, and a small group of engineers could (in theory) output a few million dollars worth of value. It might not be attractive to VCs but it could probably form a business that lets coop members lead very comfortable lives.

The core idea is Minimal task model + programmable behaviour — a small core that enables unlimited features, since each task can carry executable scripts.

Recent updates:

- VM now uses NaN-boxing technique.

- All stack values are 64-bit (u64) but encode 5 distinct types: Boolean, String, CallData, U32, and MemSlice (25-bit offset + 25-bit size).

- Added InlineVec — a vector-like structure backed by a fixed-size array. The VM stack, control stack, call stack, and jump stack now use it with defined limits. - VM has memory now (heap). Memory is simple Vec<u64>, grows dynamically, but technically length is restricted by mem_slice_val format: 25 bits payload for offset and size

Project is still in absolutely early stage.

Repo is here: https://github.com/tracyspacy/spacydo

I've read many mind-boggling stories from those appeared to be genuine OpenClaw users, like how their assistants (from useful to dramatic) (1) plan a travel and book everything; (2) started a company and build things; (3) entered stock market and lost all the money... Moltbook added more funs.

Interestingly, I cannot find a single user of OpenClaw in my familiar communities, presumbly because it takes some effort to setup and the concept of AI taking control of everything is too scary for average tech enthusiasts.

I scan through comments on HN, many of which were discussing about the ideas, but not sharing first-hand user experiences. A few HN users who did try it gave up / failed for various reasons:

- https://news.ycombinator.com/item?id=46822562 (burning too many tokens)

- https://news.ycombinator.com/item?id=46786628 (ditto + security implication)

- https://news.ycombinator.com/item?id=46762521 (installation failed due to sandboxing)

- https://news.ycombinator.com/item?id=46831031 (moltbook didn't work)

I smell hype in the air... HN users, have any of you actually run OpenClaw and let it do any things useful or interesting? Can you share your experience?

Hi HN — I’m exploring an idea and would love your feedback.

I’m a builder and user of Obsidian, validating a concept called Concerns. Today it’s only a landing page + short survey (no product yet) to test whether this pain is real.

The core idea (2–3 bullets):

- Many of us capture tons of useful info (notes/links/docs), but it rarely becomes shipped work.

- Instead of better “organization” (tags/folders), I’m exploring an “action engine” that:

  1.detects what you’re actively targetting/working on (“active projects”)

  2.surfaces relevant saved material at the right moment

  3.proposes a concrete next action (ideally pushed into your existing task tool)

Before writing code, I’m trying to pin down two things:

- Project context signals (repo/PRs? issues? tasks? calendar? a “project doc”?)

- How to close the loop: ingest knowledge → rank against active projects → emit a small set of next-actions into an existing todo tool → learn from outcomes (done/ignored/edited) and optionally write back the minimal state. The open question: what’s the cleanest feedback signal without creating noise or privacy risk? (explicit ratings vs completion events vs doc-based write-back)

What I’m asking from you:

1.Where does your “second brain” break down the most?

capture / organization / retrieval / execution (If you can, share a concrete recent example.)

2.What best represents “active project context” for you today?

  task project (Todoist/Things/Reminders)

  issues/boards (GitHub/Linear/Jira)

  a doc/wiki page (Notion/Docs)

  calendar

  "in my head"

3.What’s your hard “no” for an AI that suggests actions from your notes/links? (pick 1–2)

  privacy/data retention

  noisy suggestions / interruption

  hallucinations / wrong suggestions

  workflow change / migration cost

  pricing

  others

I can’t name a single software application or software feature that has been mooted by AI. Zero. Take Excel as an example. Not only has AI failed to replace Excel in its entirety, but it has also failed to replace any of its features. AI was simply appended as an additional feature in the form of an agentic chatbot. This has been the trend across the entire industry, and it’s why AI has failed to fundamentally transform any of our exiting software application.

Now you might ask: what about AI native applications? Well, as it turns out, most of them are essentially clones of existing software but with a chatbot slapped on top. Because of the error prone nature of AI, any application that leverages it also has to surface all of the controls required to override all of its decisions. So you end up with a traditional software application plus AI.

AI promised to transform and even replace software applications, but all it did instead was augment them with an unreliable chatbot. All of the old fields and buttons are still there, but now there’s an additional text field that you can type into and hope for the best.

I'm building a PWA for investigative journalists who need to capture evidence without leaving forensic traces on their devices (even if seized).

The architecture: 1. "Zero-Trace" Camera: Uses `getUserMedia` to stream video.

2. RAM-Only: Captures frame to an off-screen Canvas -> Blob. Never touches the filesystem or Camera Roll.

3. Client-Side Encrypt: Blob is encrypted immediately (TweetNaCl) with a public key.

4. Upload & Wipe: Encrypted blob is uploaded, then memory is nulled.

My hypothesis is this beats "Standard Camera -> Gallery -> Upload" because there are no deleted files to recover from the SSD.

Is "RAM-only" in a browser sandbox reliable enough for life-or-death privacy? What side-channels (swap files, browser cache) am I missing?

Tech stack: Next.js, Dexie, WebCrypto. Open source.

Hope I can get much feedback I want to make my photo vault app special and make an impact for the world here is the link to my app that I want to change to this www.saecretheaven.com

So I'm trying to submit a resume to Apple, but to do so, I need an AppleId. Okay. I try to sign up for one and discover my phone number has been used by someone else. I dropped into the local Apple Store (who supposedly have the ability to reset or create AppleIds) but, spoiler alert, after two hours they couldn't figure out why my phone number won't work.

Does anyone know if there's a way to create an AppleId w/o a phone number? Apple's public docs say it's impossible.

Maybe there's an email alias I can use to submit my resume instead of buying an iProduct?

I guess I could get a dirt cheap sim for my (non-apple) phone and use it for a day, just to sign up for a new AppleId. But somehow this seems... wrong. It also seems unreasonable for a company to force me to buy an $1000 device just for the privilege of submitting my resume.

[edit: Forgot to mention, I own precisely zero iProducts.]

Hi HN,

I’m working on AI agents used for software development. These agents automatically spin up short-lived app instances – for example per pull request, per task, or per experiment – each with its own temporary URL.

Auth is handled in the standard way:

- OAuth2 / OIDC

- external identity provider

- redirect URLs must be registered in advance and be static

This clashes badly with short-lived apps:

- URLs are dynamic and unpredictable

- redirect URLs can’t realistically be pre-registered

- auth becomes the only non-ephemeral part of an otherwise fully automated workflow

What I see teams doing instead:

- disabling real auth in preview environments

- routing all callbacks through a single stable environment

- using wildcard redirects or proxy setups that feel like hacks

This gets especially awkward for AI dev agents, because they assume infrastructure is disposable and fully automated – no manual IdP config in the loop.

So I’m curious:

1. If you use short-lived preview apps, how do you handle real auth?

2. Are there clean OAuth/OIDC patterns that work with dynamic URLs?

3. Is the static redirect URL assumption still the right model here?

4. What actually works in production?

Looking for real setups and failure stories, not theory.

I’m a DevOps engineer and recently started experimenting seriously with AI-assisted coding to see how useful it actually is in real work.

It checks:

- HTTPS redirects - SSL certificate validity - Mixed content - basic security headers - HTTP/3 support

AI helped a lot with speed — scaffolding, boilerplate, and quick iterations. But testing, edge cases, and reviewing security-related logic quickly reminded me that AI doesn’t replace understanding. You still own every line of code you ship.

This is mainly a learning project, not meant to replace full security scanners. I’d appreciate any feedback, bug reports, or thoughts on what’s missing or misleading.

Check it out: https://httpsornot.com

Over the last few weeks, world models have started to feel real for the first time. You can see coherent environments, long rollouts, and increasingly convincing visuals. At the same time, most of these systems are hard to run, hard to integrate, and trade interactivity for scale.

We started Overworld because we cared less about producing impressive videos and more about building worlds you can actually inhabit. That means low latency, continuous control, and systems that respond every time you act, not once per prompt.

Last week, we released Waypoint 1, a research preview of a real-time diffusion world model that runs locally. Next week, we’re releasing Waypoint 1.1 Small, which is designed to run on modern consumer GPUs and be easy to build on and modify.

Waypoint is built from scratch rather than fine-tuned from a large video model. We optimized heavily for control frequency, sparse attention, and fast inference so the system can maintain a persistent world state and respond to input at game-level frame rates. The goal was to make something developers can integrate today, not just watch as a demo.

We think this space will move fastest once world models follow a path similar to LLMs: local execution, open tooling, and fast community-driven iteration. Genie and similar systems show what’s possible at a massive scale. Our focus has been on making that future local and accessible.

We wrote more about the “immersion gap,” why interactivity matters more than visuals alone, and how we optimized the model in a recent blog post.

Code, demos, and release details are here: https://over.world/blog/the-immersion-gap

Hi HN,

I've been working on a programming language called G. It is designed to be memory-safe and extremely fast, with a focus on a tiny footprint.

The entire interpreter is written in D and weighs in at only 2.4MB. I built it because I wanted a modern scripting language that feels lightweight but has the safety of a high-level language.

Key Features:

     Small: The binary is ~2.4MB.
     Fast: Optimized for x86_64.
     Safe: Memory-safe execution.
     Std Lib: Includes std.echo, std.newline, etc.
     

I would love to get some feedback on the syntax or the architecture from the community!

I've got a side project I've worked on for a while and I'm happy with the engineering side, but I'm terrible at marketing. I've made a couple of reddit comments, shown friends who would benefit from my project, but what is the best way to get it out there?

This is just one of those narratives that people latch onto because it has a nice ring to it. Or maybe it’s because it makes AI sound less threatening and perhaps even palatable. “Don’t worry. AI is going to replace only the repetitive parts of your job.” But if you spend even a minute examining this narrative, then you will realize just how preposterous it is.

Humans have already figured out how to automate repetitive physical and digital labor, and we’ve been doing it for decades and even centuries by using machines and computing. Simply put: If it’s repetitive, then you don’t need AI to automate it.

In fact, the kinds of task we want AI to automate are precisely those that AREN’T repetitive. That was the whole god damn point of AI.

How did we go from the original purpose of AI to claiming that it will do what we’ve already been doing for decades? Where do these narratives come from, and why do people fall for them?

I love technology. But I'm no longer optimistic about the future. It seems like AI is not going to go away, and instead of building reliable software, managers seem to push people to use AI more, as long as they ship products. Everything else is being destroyed by AI: art, music, books, personal websites. Why read a blog post, when Google AI Summary can just give you the summary? Why read a book, when you can just get AI summary of it? Why pay artists for music, when you can just generate endless amount of AI music?

And even things like "doing day to day chores" are being automated away with tools like AI assistants. The only thing you are left to do is to eat and take a sh*t throughout the day. How should people make money? No idea, as in the "prosperous future", everything is replaced by AI.

So my question HN: What's the point anymore? Why keep going and where to?

"Tokens per request" has been a misleading cost model for us in production. The real drivers seem to be multipliers: retries/429s, tool fanout, P95 context growth, and safety passes.

What’s been the biggest cost multiplier in your prod LLM systems, and what policies worked (caps, degraded mode, fallback, hard fail)?

After witnessing what is being said about the AI Botlers (like OpenClaw/Moltbot/Clawdbot), I believe UIs will start melting big time.

The point, click and type era is over.

Voice will take over as the primary interface.

UIs will be adaptive and enabled on demand.

There will be an AI agent layer on every single PC out there.

Since privacy will be an issue, "Shazam-like" filters will inhibit uncleared capture of voice.

Makes sense?